The predictability of a control-loop behavior beyond its control horizon is an unambiguous indication of loop malfunctioning. Based on the dynamic complexity of the error residual time series the permutation entropy is proposed to define a sensitive index for performance monitoring using data from close-loop operation. A generic framework to understand and quantify the distinctive increase in predictability of the controller error resulting from ill-tuning, sensor errors and actuator faults using a entropy-like index is presented. The dynamic complexity of a well-performing control loop should correspond to the maximum entropy. As loop performance degrades the entropy of its residual time series decreases and any loss of dynamic complexity in the control system gives rise to an increase of the predictability of the control error time series. Results obtained using the proposed performance index for industrial data sets are presented to discuss the influence of the sample size, control horizon and entropy scaling in the assessment of close-loop performance.